Comparison
CodeBERT vs CodeGeeX
Verdict
Pick CodeBERT if codeBERT is an advanced pre-trained model for programming and natural language tasks in multiple languages like Python and Java; pick CodeGeeX if codeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration.
Markdown twin · CodeBERT alternatives · CodeGeeX alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | CodeBERT | CodeGeeX |
|---|---|---|
| Maintenance | Dormant (1123d since push) As of 2w · github_public_v1 | Dormant (719d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- CodeBERT
- CodeBERT series models for code pretraining in Python and programming languages
- CodeGeeX
- CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.
Stars
- CodeBERT
- 2.8k
- CodeGeeX
- 8.8k
Forks
- CodeBERT
- 497
- CodeGeeX
- 688
Open issues
- CodeBERT
- 86
- CodeGeeX
- 188
Language
- CodeBERT
- Python
- CodeGeeX
- Python
Adopt for
- CodeBERT
- CodeBERT is an advanced pre-trained model for programming and natural language tasks in multiple languages like Python and Java.
- CodeGeeX
- CodeGeeX is an open-source multilingual code generation model, originally developed with MindSpore and compatible with PyTorch through DeepSpeed integration.
Persona
- CodeBERT
- -
- CodeGeeX
- -
Runtime
- CodeBERT
- -
- CodeGeeX
- -
License
- CodeBERT
- MIT
- CodeGeeX
- Apache-2.0
Last pushed
- CodeBERT
- Jul 9, 2023
- CodeGeeX
- Aug 13, 2024
Categories
- CodeBERT
- Model Training
- CodeGeeX
- LLM Frameworks, Model Training
Trust and health
Days since push
- CodeBERT
- 1123d
- CodeGeeX
- 719d
Open issues (now)
- CodeBERT
- 86
- CodeGeeX
- 188
OSV dependency advisories
- CodeBERT
- No lockfile (source not queried)
- CodeGeeX
- Published findings
Full report
- CodeBERT
- Trust report
- CodeGeeX
- Trust report
Shared compatibility
- Python · CodeBERT: Python runtime · CodeGeeX: Python runtime
Choose CodeBERT if…
- License: CodeBERT is MIT, CodeGeeX is Apache-2.0.
- Requirements: Install torch and transformers via pip before using CodeBERT for embedding generation or other tasks; Ensure Python and Hugging Face's transformers framework are available, as they form the core execution environment for utilizing this model.
- Tags unique to CodeBERT: code pretraining, transformers framework.
- When you need to work on tasks involving both programming and natural language processing across six different programming languages: Python, Java, JavaScript, PHP, Ruby, Go
When NOT to use CodeBERT
- Avoid for direct mask prediction tasks without MLM (Masked Language Model) fine-tuning as CodeBERT is not natively equipped for such tasks unlike its variant designed with MLM capabilities
- Do not consider it if your project requires pre-training models on a wider variety of programming languages beyond the six supported by this model
Choose CodeGeeX if…
- License: CodeGeeX is Apache-2.0, CodeBERT is MIT.
- Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models.
- Also covers LLM Frameworks.
- When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability.
When NOT to use CodeGeeX
- If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+.
- In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (microsoft/CodeBERT) · observed Aug 5, 2026
- GitHub forks (microsoft/CodeBERT) · observed Aug 5, 2026
- Last push (microsoft/CodeBERT) · observed Jul 9, 2023
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zai-org/CodeGeeX) · observed Aug 2, 2026
- GitHub forks (zai-org/CodeGeeX) · observed Aug 2, 2026
- Last push (zai-org/CodeGeeX) · observed Aug 13, 2024
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: CodeBERT 2.8k · CodeGeeX 8.8k (synced Aug 5, 2026).
Common questions
- What is the difference between CodeBERT and CodeGeeX?
- CodeBERT: CodeBERT series models for code pretraining in Python and programming languages. CodeGeeX: CodeGeeX is an open multilingual code generation model implemented in Mindspore and available via PyTorch.. See the comparison table for live GitHub stats and shared categories.
- When should I choose CodeBERT over CodeGeeX?
- Choose CodeBERT over CodeGeeX when License: CodeBERT is MIT, CodeGeeX is Apache-2.0; Requirements: Install torch and transformers via pip before using CodeBERT for embedding generation or other tasks; Ensure Python and Hugging Face's transformers framework are available, as they form the core execution environment for utilizing this model; Tags unique to CodeBERT: code pretraining, transformers framework; When you need to work on tasks involving both programming and natural language processing across six different programming languages: Python, Java, JavaScript, PHP, Ruby, Go.
- When should I choose CodeGeeX over CodeBERT?
- Choose CodeGeeX over CodeBERT when License: CodeGeeX is Apache-2.0, CodeBERT is MIT; Tags unique to CodeGeeX: ai programming tools, code generation, pretrained-models; Also covers LLM Frameworks; When you require support for multilingual code generation and your project has a Python-based infrastructure with CUDA GPU availability.
- When should I avoid CodeBERT?
- Avoid for direct mask prediction tasks without MLM (Masked Language Model) fine-tuning as CodeBERT is not natively equipped for such tasks unlike its variant designed with MLM capabilities Do not consider it if your project requires pre-training models on a wider variety of programming languages beyond the six supported by this model
- When should I avoid CodeGeeX?
- If your development environment lacks the necessary dependencies like Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+. In scenarios where an open-source solution is not preferable or when support for exclusively one language's syntax is sufficient.
- Is CodeBERT or CodeGeeX more popular on GitHub?
- CodeGeeX has more GitHub stars (8,809 vs 2,787). Stars measure visibility, not whether either tool fits your constraints.
- Are CodeBERT and CodeGeeX open source?
- Yes - both are open-source projects on GitHub (CodeBERT: MIT, CodeGeeX: Apache-2.0).
- Where can I find alternatives to CodeBERT or CodeGeeX?
- GraphCanon lists graph-backed alternatives at CodeBERT alternatives and CodeGeeX alternatives (CodeBERT markdown twin, CodeGeeX markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, CodeBERT or CodeGeeX?
- CodeBERT: Dormant. CodeGeeX: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for CodeBERT and CodeGeeX?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: CodeBERT trust report; CodeGeeX trust report.